Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest

The United States Forest Service Forest Inventory and Analysis (FIA) Program provides a diverse selection of data used to assess the status of the nation’s forests using sample locations dispersed throughout the country. Airborne laser scanning (ALS) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize forest structure in three dimensions. This study investigates the potential of discrete return ALS data for modeling forest aboveground biomass (AGBM) and gross volume (gV) at FIA plot locations in the Malheur National Forest, eastern Oregon utilizing three analysis levels: (1) individual subplot (r = 7.32 m); (2) plot, comprising four clustered subplots; and (3) hectare plot (r = 56.42 m). A methodology for the creation of three point cloud-based airborne LiDAR metric sets is presented. Models for estimating AGBM and gV based on LiDAR-derived height metrics were built and validated utilizing FIA estimates of AGBM and gV derived using regional allometric equations. Simple linear regression models based on the plot-level analysis out performed subplot-level and hectare-level models, producing R2 values of 0.83 and 0.81 for AGBM and gV, utilizing mean height and the 90th height percentile as predictors, respectively. Similar results were found for multiple regression models, where plot-level analysis produced models with R2 values of 0.87 and 0.88 for AGBM and gV, utilizing multiple height percentile metrics as predictor variables. Results suggest that the current FIA plot design can be used with dense airborne LiDAR data to produce area-based estimates of AGBM and gV, and that the increased spatial scale of hectare plots may be inappropriate for modeling AGBM of gV unless exhaustive tree tallies are available. Overall, this study demonstrates that ALS data can be used to create models that describe the AGBM and gV of Pacific Northwest FIA plots and highlights the potential of estimates derived from ALS data to augment current FIA data collection procedures by providing a temporary intermediate estimation of AGBM and gV for plots with outdated field measurements

Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data

Leaf area index and aboveground biomass estimation of Populus and its hybrids using terrestrial LiDAR

Short rotation woody crops (SRWC) eastern cottonwood (Populus deltoides) and hybrid poplar plantations were established in 2021 in Pontotoc and Oktibbeha counties of Mississippi to study the biomass potential of SRWC for biofuel production. We used a novel backpack LiDAR system to measure forest metrics and harvested sample trees to build aboveground biomass (AGB) and leaf area index (LAI) equations. The results showed that LiDAR-derived variables accurately estimated aboveground biomass (R2 =0.81 and 29.22 % RMSE). However, the LAI estimation results showed that the LiDAR metrics moderately explained field measurements of LAI (R2 =0.31 and 18.05% RMSE) for individual-trees and poorly explained plot-level LAI measured with the LAI-2200C (R2 =0.11 and 66% RMSE). The backpack LiDAR system can be valuable for forest managers and researchers, enabling non-destructive AGB and LAI estimation. However, further research is required to overcome its limitations and achieve precise measurements of AGB and LAI

Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data

Light Detection and Ranging (LiDAR) remote sensing has been established as one of the most promising tools for large-scale forest monitoring and mapping. Continuous advances in computational techniques, such as machine learning algorithms, have been increasingly improving our capability to model forest attributes accurately and at high spatial and temporal resolution. While there have been previous studies exploring the use of LiDAR and machine learning algorithms for forest inventory modeling, as yet, no studies have demonstrated the combined impact of sample size and different modeling techniques for predicting and mapping stem total volume in industrial Eucalyptus spp. tree plantations. This study aimed to compare the combined effects of parametric and nonparametric modeling methods for estimating volume in Eucalyptus spp. tree plantation using airborne LiDAR data while varying the reference data (sample size). The modeling techniques were compared in terms of root mean square error (RMSE), bias, and R2 with 500 simulations. The best performance was verified for the ordinary least-squares (OLS) method, which was able to provide comparable results to the traditional forest inventory approaches using only 40% (n = 63; ~0.04 plots/ha) of the total field plots, followed by the random forest (RF) algorithm with identical sample size values. This study provides solutions for increasing the industry efficiency in monitoring and managing forest plantation stem volume for the paper and pulp supply chain

Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems

100022Background: The characterization of surface and canopy fuel loadings in fire-prone pine ecosystems is critical for understanding fire behavior and anticipating the most harmful ecological effects of fire. Nevertheless, the joint consideration of both overstory and understory strata in burn severity assessments is often dismissed. The aim of this work was to assess the role of total, overstory and understory pre-fire aboveground biomass (AGB), estimated by means of airborne Light Detection and Ranging (LiDAR) and Landsat data, as drivers of burn severity in a megafire occurred in a pine ecosystem dominated by Pinus pinaster Ait. in the western Mediterranean Basin. Results: Total and overstory AGB were more accurately estimated (R2 equal to 0.72 and 0.68, respectively) from LiDAR and spectral data than understory AGB (R2 ¼ 0.26). Density and height percentile LiDAR metrics for several strata were found to be important predictors of AGB. Burn severity responded markedly and non-linearly to total (R2 ¼ 0.60) and overstory (R2 ¼ 0.53) AGB, whereas the relationship with understory AGB was weaker (R2 ¼ 0.21). Nevertheless, the overstory plus understory AGB contribution led to the highest ability to predict burn severity (RMSE ¼ 122.46 in dNBR scale), instead of the joint consideration as total AGB (RMSE ¼ 158.41). Conclusions: This study novelty evaluated the potential of pre-fire AGB, as a vegetation biophysical property derived from LiDAR, spectral and field plot inventory data, for predicting burn severity, separating the contribution of the fuel loads in the understory and overstory strata in Pinus pinaster stands. The evidenced relationships between burn severity and pre-fire AGB distribution in Pinus pinaster stands would allow the implementation of threshold criteria to support decision making in fuel treatments designed to minimize crown fire hazard.S

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps

Background: Estimation of forest biomass on the regional and global scale is of great importance. Many studies have demonstrated that lidar is an accurate tool for estimating forest aboveground biomass. However, results vary with forest types, terrain conditions and the quality of the lidar data. Methods: In this study, we investigated the utility of low density lidar data (<2 points∙m−2) for estimating forest aboveground biomass in the mountainous forests of northern Italy. As a study site we selected a 4 km2 area in the Valsassina mountains in Lombardy Region. The site is characterized by mixed and broad-leaved forests with variable stand densities and tree species compositions, being representative for the entire Pre-Alps region in terms of type of forest and geomorphology. We measured and determined tree height, DBH and tree species for 27 randomly located circular plots (radius =10 m) in May 2008. We used allometric equations to calculate total aboveground tree biomass and subsequently plot-level aboveground biomass (mg∙ha−1). Lidar data were collected in June 2004. Results: Our results indicate that low density lidar data can be used to estimate forest aboveground biomass with acceptable accuracies. The best height results show a R2 = 0.87 from final model and the root mean square error (RMSE) 1.02 m (8.3% of the mean). The best biomass model explained 59% of the variance in the field biomass. Leave-one-out cross validation yielded an RMSE of 30.6 mg∙ha−1 (20.9% of the mean). Conclusions: Low-density lidar data can be used to develop a forest aboveground biomass model from plot-level lidar height measurements with acceptable accuracies. In order to monitoring the National Forest Inventory, and respond to Kyoto protocol requirements, this analysis might be applied to a larger area. Keywords: LiDAR; Allometric equations; Plant height; Mixed fores